Proposals for detecting defects in textile webs are described in the Textile Research Journal 63(4), pages 244-246 (1993) and 66(7), pages 474-482 (1996) under the titles: "Assessment of Set Marks by Means of Neural Nets" and "Automatic Inspection of Fabric Defects Using an Artificial Neural Network Technique". According to these publications, neural networks can be used for the detection of defects in textiles. In the disclosed methods particular input values are first determined for the network. Such input values include, for example, the distance between threads in the fabric at a given site or the mean value of this distance over the entire fabric, the standard deviation from values for the distance, the yarn mass and intensity values, which are derived from a fabric image subjected to Fourier transformation. These are all measurement values which must first be obtained from values derived from the fabric by way of more or less extensive calculations.
A disadvantage of methods of this type resides in the fact that they are not very flexible, so that the detection of defects in different fabrics requires calculations which need to be carried out in advance. Thus, it is not possible to derive or deduce input values from the web for a defect detection system which are adequate for all possible types of web texture. If an approximation of this method is nevertheless to be achieved, then a very large number of different measurement values must be determined, resulting in a correspondingly high calculation outlay. High speed and high cost computers are required to this end.
A method for detecting errors in lace is disclosed in Sanby et al, "The Automated Inspection of Lace Using Machine Vision," Mechatronics, Vol. 5, No. 2/03, Mar. 1, 1995. In this method, values for the intensity or brightness of scanning points of a picture of the original lace are compared to those of an error-free or reference picture. Values of the differences are calculated and fed to a threshold stage. Scanning points of the picture showing greatly differing values trigger an output signal. Such trigger signals are especially generated in the region of errors in the lace. Due to geometric distortion of the pictures, many apparent (but not real) errors will be detected or signaled. Therefore, a neural network is used for discriminating apparent errors from real errors. Scanning points belonging to a small area surrounding one trigger pixel are fed to the neural network, which acts as a classifier. The network is also fed with pixels and corresponding brightness values from the original picture and from the reference picture. From these three sets of pixels, the network determines if the trigger pixel is really indicating an error or not.
One drawback of this method resides in the fact that first a preliminary discrimination of errors in the lace is performed by comparing brightness values of pixels alone. Subsequently, the neural network confirms the preliminary discrimination. For that task, three sets of data must be fed to the network in order to obtain a definitive judgment. This method is not suited for inspecting textiles such as woven fabric or other types of cloth which do not show a periodically repeating structure in an image. Compared to lace, such textiles have textures wherein errors are only distinguished by modifications of the texture. Methods using differences in images do not give acceptable results. A subsequent classification in neural networks is not useful in this context.
The present invention attains the object of providing a method and a device which can be rapidly adapted to widely varying textile webs and is simple to operate.
This objective is attained by way of the skillful use of modern, cost-effective computers operating in parallel. The web is scanned in known manner, for example line-by-line, by a camera which supplies data to a memory. Values for the brightness or intensity of scanning points or partial areas of a web are stored in the memory. In this manner, the memory eventually contains an image of a section of the web. Values from connected areas are then retrieved in parallel from the memory and supplied in parallel to a neural network, which is trained to recognize defects. The neural network indicates whether there is a defect in the examined area. This result is read into a further memory, which stores this result, taking into account the position of the area on the web. As the examined areas gradually cover the entire width of the web and therefore also cover the web over a section of its longitudinal direction, conclusive data regarding defects in the examined section is eventually available.
In accordance with the invention, a neural network of a type that is known per se is used as a non-linear filter and operates directly with brightness values from a relatively large environment (e.g. 10.times.100 pixels) as input values for the neural network, without the need for additional measurement values. The environment is displaced pixel-by-pixel over the surface of the web, so that a filtering operation is carried out. At the output of the neural network a filtered image of the examined area is produced, in which the novel structure of the fabric is attenuated and errors are clearly identified. By means of a learning process, both the filter structure and the filter parameters are automatically determined and in this manner adapted to any type of textured and small-patterned surfaces. The learning process can be effected by the presentation of approximately 20 to 100 image patterns which contain defects, and the same number of image patterns containing no defects. By dividing the filter into two neural networks for input environments which are oriented in the warp or weft direction in the case of wovens, the distinction between warp and weft defects can be further supported.
The advantages attained by this invention can be seen in particular in that a device of this type can be constructed from cost-effective, simple computers which operate in parallel and are optimized for neural networks. As a result of the parallel processing of all input values, very high computing capacities (e.g. several Giga MAC (multiply accumulate calculations)) are attained, so that the result of the examination can also be continuously determined even at high product web velocities. Computers of this type can be extensively integrated in a single silicon chip and used in the form of add-on boards in personal computers. Examples of circuit boards of this type would be the PALM PC board made by the company Neuroptic Technologies, Inc and the CNAPS PC board made by the company Adaptive Solutions. In this manner, high inspection speeds of, for example, 120 m/min are possible.
The learning process can be effected very simply with the aid of a web section recognized as defect-free by the eye and defective sections of the web. In addition, the sensitivity of the defect detection can be increased by the particular form and orientation of the areas from which input values are derived. Using a simple learning process, a high degree of adaptability to differently textured webs is possible. No specially trained personnel are required for the simple operating procedure. The invention can be used for textured and patterned surfaces.